A hybrid knowledge-based system applied to epidemic screening |
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Authors: | Lynn Ling X Li |
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Affiliation: | College of Business Administration, Butler University, Indianapolis, USA |
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Abstract: | Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging. |
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Keywords: | hybrid knowledge-based systems model-based reasoning case-based reasoning rule-based reasoning |
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